Bidirectional ConvLSTM for bias correction in subseasonal‐to‐seasonal temperature forecast with terrain feature over mainland China

Abstract Subseasonal to seasonal (S2S) forecasting is crucial for early weather disaster prevention. Numerical weather prediction outputs often have systematic biases that need correction. To address this issue, the deep‐learning model BiConvLSTM harnesses spatiotemporal data to correct the 2‐m air temperature forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) over mainland China, employing ECMWF Reanalysis v5 data as the labeled dataset. BiConvLSTM improves upon the ConvLSTM by incorporating a bidirectional arrangement. Additionally, the terrain feature is used to further boost correction accuracy. Experimental results show that our model outperforms three benchmark models, the ensemble mean (EMN) and the model output statistic based on ensemble members, EDConvLSTM across the mean absolute error (MAE), mean squared skill score (MSSS), and two anomaly correlation coefficients, the pattern correlation coefficient and temporal correlation coefficient. The differences in MAE and MSSS between BiConvLSTM and EMN gradually decrease with increasing forecast lead times but begin to widen after 16 days, indicating that BiConvLSTM enhances the predictive accuracy of S2S forecasts. To better understand the sources of model prediction errors, an error decomposition based on mean‐squared error is also conducted. Results indicate that all post‐processing models are capable of reducing the bias of the ECMWF. Deep‐learning models can lower sequence errors, whereas the BiConvLSTM effectively reduces distribution errors, particularly in high‐latitude regions. BiConvLSTM also reduces errors in the first 20 days of lead times observed in ConvLSTM by traversing sequences bidirectionally and shows enhanced performance beyond 16 days with terrain improvements compared with EMN.